Energy intake estimation from counts of chews and swallows

Abstract

Current, validated methods for dietary assessment rely on self-report, which tends to be inaccurate, time-consuming, and burdensome. The objective of this work was to demonstrate the suitability of estimating energy intake using individually-calibrated models based on Counts of Chews and Swallows (CCS models). In a laboratory setting, subjects consumed three identical meals (training meals) and a fourth meal with different content (validation meal). Energy intake was estimated by four different methods: weighed food records (gold standard), diet diaries, photographic food records, and CCS models. Counts of chews and swallows were measured using wearable sensors and video analysis. Results for the training meals demonstrated that CCS models presented the lowest reporting bias and a lower error as compared to diet diaries. For the validation meal, CCS models showed reporting errors that were not different from the diary or the photographic method. The increase in error for the validation meal may be attributed to differences in the physical properties of foods consumed during training and validation meals. However, this may be potentially compensated for by including correction factors into the models. This study suggests that estimation of energy intake from CCS may offer a promising alternative to overcome limitations of self-report.

Validation of an inexpensive and accurate mathematical method to measure long-term changes in free-living energy intake

“…Traditional measurements of EI rely on self-reports that are notoriously inaccurate and may be insufficient for drawing scientific conclusions (2)(3)(4). Several technologies are under development for self-tracking food intake, including remote food photography (5,6) as well as devices that count bites (7,8) or measure chewing and swallowing (7). However, continuous tracking of diet by using such technologies over extended time periods is likely to be onerous and impractical and will still rely on self-report to some extent.…”

Segmentation and Characterization of Chewing Bouts by Monitoring Temporalis Muscle Using Smart Glasses With Piezoelectric Sensor

“…studies which rely on modification of eating behavior by increasing the number of chews per bite or modifying the chewing rate [5]–[10]. Other studies have proposed the use of chew counts or chewing rate for estimation of mass per bite [12] or energy intake estimation in a meal [11]. In order to automate these systems/approaches, there is a need to developed methods for accurate estimation of chew counts.…”

“…The counts of chews and chewing rate can also be used for estimation of ingested mass and energy intake. In [11], individually calibrated models were proposed for estimation of the ingested mass and caloric energy intake from the chew counts. Chewing rate estimated from chewing sounds captured through a miniature microphone was proposed for estimating weight per bite for three different food types [12].…”

“…Thus, there is a need for objective and automatic methods for quantification of the chewing behavior of individuals. In [11], chewing sounds were used for estimation of chewing sequences and chewing microstructure with a recall of 80%. However, acoustic-based approaches are susceptible to surrounding acoustic noise or speech.…”

Audio-based detection and evaluation of eating behavior using the smartwatch platform

“…Therefore, a fundamental question in the field of electronic food monitoring is the validity of chew and swallow counts as a heuristic for estimation of Caloric intake. A recent work by Fontana et al [5] addresses this issue by comparing several different techniques for estimation of Caloric intake: weighed food records (gold standard), diet diaries, and electronic sensor-based measurements of chews and swallows. Though the study was conducted under constrained conditions, the results suggest that chew and swallow counts may be a promising alternative to manual self-reporting techniques.…”